Is the Normal Heart Rate Chaotic?

In cooperation with

Is the Normal Heart Rate Chaotic?
Data for study

In its June 2008 issue, the editors of
Chaos announced a new feature,
"Controversial Topics in Nonlinear Dynamics". The first controversial
topic to be aired is "Is the Normal Heart Rate Chaotic?" (follow the link
to an essay by Leon Glass summarizing the debate, and links to eight papers
published in Chaos in its June 2009 issue, in response to the question).

Since major impediments to achieving consensus on the statistical
properties of biologic time series have included the lack of open
access time series, PhysioNet has provided (below) a set of 15 heart
beat (RR-interval) time series in health (series n1rr, n2rr, n3rr, n4rr, and
n5rr) and disease (congestive heart failure: c1rr, c2rr, c3rr, c4rr, and c5rr;
and atrial fibrillation: a1rr, a2rr, a3rr, a4rr, and a5rr). Each time series is
about 24 hours long (roughly 100,000 intervals), and is provided
as a text file for analysis (for example, n1rr.txt) and as an image
for visual review (for example, n1rr.pdf).

The time series belonging to the first two groups (healthy and
congestive heart failure) are all in sinus rhythm. Those in the third
group (a1rr, a2rr, a3rr, a4rr, and a5rr) are provided as examples of a
cardiac rhythm that is not sinus rhythm; in that group, the rhythm is
atrial fibrillation (AF), an atrial arrhythmia producing an erratic
and typically rapid ventricular response. All of the time series were
derived from continuous ambulatory (Holter) electrocardiograms (ECGs)
that are available elsewhere on the PhysioNet web site; see the text
file RECORDS (below) for additional
information about the sources, including where to find the original
ECGs and beat annotations from which these series were derived. For
each of the 10 healthy and CHF time series, the RECORDS file
also indicates the time of day corresponding to the beginning of the
time series, and the gender and age of the subject. This information
is not available for any of the 5 AF time series, nor are there
annotations for any of the time series with respect to activity level
and sleep. Sleeping hours in healthy subjects, however, reliably
correspond to sustained periods during which the inter-beat intervals
are consistently relatively long for that individual.

Each line in the *rr.txt files contains information about one RR interval,
in three fields:

the length of the RR interval, in seconds;

a code indicating the type of heart beat that ends the RR interval
(N is normal, and anything else is abnormal); and

the elapsed time, in seconds, from the beginning of the time series
to the end of the RR interval.

In general, intervals that precede and follow abnormal beats should be
considered as outliers; abnormal beats are rare, except in the CHF
cases, which contain up to 2% abnormal beats. Occasional beats that
were not detected as a result of signal loss or noise result in
abnormally long intervals, and (rarely) an artifact has been
misdetected as a beat, resulting in abnormally short intervals; these
events should also be considered as outliers.

Since many methods for characterizing the dynamics of time series are
extremely sensitive to outliers, and since outlier detection in these
time series is non-trivial, we have also provided a set of "filtered"
time series from which almost all of the outliers have been removed (using
the nguess software available
here as part of the WFDB software
package). To the extent possible, these series contain only intervals
between consecutive normal (N) heart beats, and they are therefore designated
as the "nn" series. Series n1nn is the "filtered" version of series n1rr,
etc. As for the "rr" series, the "nn" series are provided as text
files (n1nn.txt, etc.) and as image files (n1nn.pdf, etc),
in the same formats as for the "rr" series files.

By choosing to use the "nn" time series, you may be able to avoid
having to deal with outliers in your analysis, but you may be able to
get better results starting with the "rr" series, applying a more (or
less) agressive outlier rejection strategy that is better matched to
the characteristics of your analytic methods. In any case, it may be
helpful to refer to both the "rr" and the "nn" series in order to
assess how outliers influence your results.

We encourage participants in the Chaos Controversies issue to use the
15 RR interval time series provided here as a common focus of
analysis. If you use other time series, we hope you will be willing to
make them freely available at the PhysioNet website so that other
participants can apply their analyses to them. Contributions of
additional open-source implementations of algorithms related to this
topic are also encouraged so that any differences in analyses and
conclusions can be more readily understood. Credit will be given to
the contributors. Please
write to us if you wish
to contribute data or software. Use of these data sets, or
contributions of data or algorithms to PhysioNet, will not be criteria
for acceptance of an article, however.